Feasibility of Standardizing Pre-operative Assessment Clinics Across a Hospital System
Bibliographic record
Abstract
Pre-operative assessments, which include patient history and physical examination, are fundamental in ensuring patient education about their procedure, and leads to successful post-operative outcomes. Within Niagara Health (NH), there are three main hospital sites where operations occur. Currently, there is inconsistency in the pre-operative assessments between sites for the same surgical procedures, demonstrated by variation in pre-operative assessment times, activities, and information given to patients. The aim of this project is to understand where standardization through quality improvement (QI) initiatives should begin within these pre-operative assessment clinics and determine the feasibility of standardization across varying hospital sites. To achieve this aim, Plan, Do, Study, Act (PDSA) cycles were conducted and involved structured observations at each site to gain a comprehensive understanding of pre-operative practices across sites. Root cause analysis found moderate correlation at two sites and strong correlation at one site between patient age and consult time. Affinity analysis determined that the most pragmatic and feasible area for improvement was through standardization of admission history forms. While the piloting of a new standardized form showed no significant increase in consult times, fundamental barriers such as nursing staff turnover, lack of familiarity with the new form, and concerns of comprehensiveness prevented the continuation of this new standardized form. Future attempts at standardization should begin with collaboration and co-design with pre-op clinic staff, followed by identification of elements of the complex adaptive system that can feasibly be standardized to reduce unnecessary variation while at the same time increasing buy-in for form use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.101 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".